6,217 research outputs found
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a robot
needs to be able to reactively adapt to changes arising in its environment. The
environment changes are usually reflected in deviation from expected sensory
traces. These deviations in sensory traces can be used to drive the motion
adaptation, and for this purpose, a feedback model is required. The feedback
model maps the deviations in sensory traces to the motion plan adaptation. In
this paper, we develop a general data-driven framework for learning a feedback
model from demonstrations. We utilize a variant of a radial basis function
network structure --with movement phases as kernel centers-- which can
generally be applied to represent any feedback models for movement primitives.
To demonstrate the effectiveness of our framework, we test it on the task of
scraping on a tilt board. In this task, we are learning a reactive policy in
the form of orientation adaptation, based on deviations of tactile sensor
traces. As a proof of concept of our method, we provide evaluations on an
anthropomorphic robot. A video demonstrating our approach and its results can
be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on
Robotics and Automation (ICRA) 201
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Robots need to be able to adapt to unexpected changes in the environment such
that they can autonomously succeed in their tasks. However, hand-designing
feedback models for adaptation is tedious, if at all possible, making
data-driven methods a promising alternative. In this paper we introduce a full
framework for learning feedback models for reactive motion planning. Our
pipeline starts by segmenting demonstrations of a complete task into motion
primitives via a semi-automated segmentation algorithm. Then, given additional
demonstrations of successful adaptation behaviors, we learn initial feedback
models through learning from demonstrations. In the final phase, a
sample-efficient reinforcement learning algorithm fine-tunes these feedback
models for novel task settings through few real system interactions. We
evaluate our approach on a real anthropomorphic robot in learning a tactile
feedback task.Comment: Submitted to the International Journal of Robotics Research. Paper
length is 21 pages (including references) with 12 figures. A video overview
of the reinforcement learning experiment on the real robot can be seen at
https://www.youtube.com/watch?v=WDq1rcupVM0. arXiv admin note: text overlap
with arXiv:1710.0855
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Interactive Imitation Learning in Robotics: A Survey
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL)
where human feedback is provided intermittently during robot execution allowing
an online improvement of the robot's behavior. In recent years, IIL has
increasingly started to carve out its own space as a promising data-driven
alternative for solving complex robotic tasks. The advantages of IIL are its
data-efficient, as the human feedback guides the robot directly towards an
improved behavior, and its robustness, as the distribution mismatch between the
teacher and learner trajectories is minimized by providing feedback directly
over the learner's trajectories. Nevertheless, despite the opportunities that
IIL presents, its terminology, structure, and applicability are not clear nor
unified in the literature, slowing down its development and, therefore, the
research of innovative formulations and discoveries. In this article, we
attempt to facilitate research in IIL and lower entry barriers for new
practitioners by providing a survey of the field that unifies and structures
it. In addition, we aim to raise awareness of its potential, what has been
accomplished and what are still open research questions. We organize the most
relevant works in IIL in terms of human-robot interaction (i.e., types of
feedback), interfaces (i.e., means of providing feedback), learning (i.e.,
models learned from feedback and function approximators), user experience
(i.e., human perception about the learning process), applications, and
benchmarks. Furthermore, we analyze similarities and differences between IIL
and RL, providing a discussion on how the concepts offline, online, off-policy
and on-policy learning should be transferred to IIL from the RL literature. We
particularly focus on robotic applications in the real world and discuss their
implications, limitations, and promising future areas of research
Space power distribution system technology. Volume 2: Autonomous power management
Electrical power subsystem requirements, power management system functional requirements, algorithms, power management subsystem, hardware development, and trade studies and analyses are discussed
Fourth Conference on Artificial Intelligence for Space Applications
Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming
Defense Acquisition and Budgeting: Investigating the Adequacy of Linkage Between Systems
In this article we assess evidence and test the hypothesis that the complicated
architecture and processes of national defense planning, programming, budgeting and
execution and the defense acquisition decision system produce system linkage weaknesses
that lead to unintended and negative consequences for defense acquisition and
procurement. The purpose of this article is to identify key points of linkage weakness and
failure between DOD financial management and acquisition decision systems, and then
suggest how reengineering and realignment might be approached to resolve some of
these problems. We first describe the key components of the defense planning, program,
budgeting and execution system (PPBES) decision process. We then provide an analysis
of recent changes to PPBES. Next, we describe the defense acquisition system (DAS) in
detail. Then, relying on independent assessment of system relationships and data
gathered from interviews with system participants, we identify systems linkages and areas
of misalignment between the PPBES and the DAS. Finally, we provide conclusions with
respect to our hypothesis and analysis of consequent key problems and issues to be
addressed by top level DOD leadership
Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition
Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this
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